Last updated: 2022-03-14

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Knit directory: historicVScurrentFire/

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Rmd 6378d80 rswaty 2022-03-14 added OK Swamp historical fire
html 6378d80 rswaty 2022-03-14 added OK Swamp historical fire
Rmd 277f160 rswaty 2022-03-10 inital commit
html 277f160 rswaty 2022-03-10 inital commit

The inputs and GIS methods (briefly)

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Historical Fire for the Huron-Manistee National Forest

As mentioned above we generated a big messy table for our exploration. You can explore the raw table minus VALUE fields here. It’s huge, even though the landscape is not that large.

Wrangle the data

The above data has too much information and is not formatted properly to explore historical fire regimes for our landscape. We did some wranging. Hit the “Code” button at the top-right of the page to see the code.

Take look at historical ecosystems

LANDFIRE data is designed for use over large areas. It’s a good idea to review initial outputs before proceeding. For example here we want to see which Biophysical Settings were most prevalent before we go to calculate historical fire regimes.

From this we see:

  1. There were a mix of highly fire-dependent (e.g., Laurentian-Acadian Northern Pine(-Oak) Forest) and much less fire-adapted ecosystems (Laurentian-Acadian Northern Hardwoods Forest-Hemlock) on the landscape historically.
  2. Reviewing this chart and LANDFIRE model descriptions and metadata we decide to remove all BpSs except the top five for this initial assessment. Here’s why:
    • Barren-Rock/Sand/Clay, Open Water and sparsely vegetated systems (such as the Western Great Plains Sparsely Vegetated Systems) do not have modeled historical fire regimes
    • These five represented over 90% of the landscape and represent a wide variety of edaphic and fire conditions.

The historical annual acres burned chart will only represent the top five BpSs by acreage.

The Northwestern Great Plains Mixedgrass Prairie had a historical Mean Fire Return Interval of ~13 years, which in turn meant that ~8% of that ecosystem burned every year, or almost 6k acres for this landscape.

Historical Fire for the Okefeenokee Swamp

As mentioned above we generated a big messy table for our exploration. You can explore the raw table minus VALUE fields here. It’s huge, even though the landscape is not that large.

Wrangle the data

The above data has too much information and is not formatted properly to explore historical fire regimes for our landscape. We did some wranging. Hit the “Code” button at the top-right of the page to see the code.

Take look at historical ecosystems

LANDFIRE data is designed for use over large areas. It’s a good idea to review initial outputs before proceeding. For example here we want to see which Biophysical Settings were most prevalent before we go to calculate historical fire regimes.

From this we see:

The historical annual acres burned chart will only represent the top five BpSs by acreage.


sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19043)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.1252 
[2] LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] forcats_0.5.1   dplyr_1.0.7     purrr_0.3.4     readr_2.1.2    
 [5] tidyr_1.2.0     tibble_3.1.6    ggplot2_3.3.5   tidyverse_1.3.1
 [9] stringr_1.4.0   scales_1.1.1    DT_0.20         workflowr_1.7.0

loaded via a namespace (and not attached):
 [1] httr_1.4.2        sass_0.4.0        bit64_4.0.5       vroom_1.5.7      
 [5] jsonlite_1.7.3    modelr_0.1.8      bslib_0.3.1       assertthat_0.2.1 
 [9] getPass_0.2-2     highr_0.9         cellranger_1.1.0  yaml_2.2.2       
[13] pillar_1.7.0      backports_1.4.1   glue_1.6.1        digest_0.6.29    
[17] promises_1.2.0.1  rvest_1.0.2       colorspace_2.0-3  htmltools_0.5.2  
[21] httpuv_1.6.5      pkgconfig_2.0.3   broom_0.7.12      haven_2.4.3      
[25] processx_3.5.2    whisker_0.4       later_1.3.0       tzdb_0.2.0       
[29] git2r_0.29.0      generics_0.1.2    farver_2.1.0      ellipsis_0.3.2   
[33] withr_2.4.3       cli_3.1.1         magrittr_2.0.2    crayon_1.4.2     
[37] readxl_1.3.1      evaluate_0.14     ps_1.6.0          fs_1.5.2         
[41] fansi_1.0.2       xml2_1.3.3        tools_4.1.2       hms_1.1.1        
[45] lifecycle_1.0.1   munsell_0.5.0     reprex_2.0.1      callr_3.7.0      
[49] compiler_4.1.2    jquerylib_0.1.4   rlang_1.0.1       grid_4.1.2       
[53] rstudioapi_0.13   htmlwidgets_1.5.4 crosstalk_1.2.0   labeling_0.4.2   
[57] rmarkdown_2.11    gtable_0.3.0      DBI_1.1.2         R6_2.5.1         
[61] lubridate_1.8.0   knitr_1.37        fastmap_1.1.0     bit_4.0.4        
[65] utf8_1.2.2        rprojroot_2.0.2   stringi_1.7.6     parallel_4.1.2   
[69] Rcpp_1.0.8        vctrs_0.3.8       dbplyr_2.1.1      tidyselect_1.1.1 
[73] xfun_0.29